Epidemiology, resistance genomics and susceptibility of Acinetobacter species: results from the 2020 Spanish nationwide surveillance study

Background As increasing antibiotic resistance in Acinetobacter baumannii poses a global healthcare challenge, understanding its evolution is crucial for effective control strategies. Aim We aimed to evaluate the epidemiology, antimicrobial susceptibility and main resistance mechanisms of Acinetobacter spp. in Spain in 2020, and to explore temporal trends of A. baumannii. Methods We collected 199 single-patient Acinetobacter spp. clinical isolates in 2020 from 18 Spanish tertiary hospitals. Minimum inhibitory concentrations (MICs) for nine antimicrobials were determined. Short-read sequencing was performed for all isolates, and targeted long-read sequencing for A. baumannii. Resistance mechanisms, phylogenetics and clonality were assessed. Findings on resistance rates and infection types were compared with data from 2000 and 2010. Results Cefiderocol and colistin exhibited the highest activity against A. baumannii, although colistin susceptibility has significantly declined over 2 decades. A. non-baumannii strains were highly susceptible to most tested antibiotics. Of the A. baumannii isolates, 47.5% (56/118) were multidrug-resistant (MDR). Phylogeny and clonal relationship analysis of A. baumannii revealed five prevalent international clones, notably IC2 (ST2, n = 52; ST745, n = 4) and IC1 (ST1, n = 14), and some episodes of clonal dissemination. Genes bla OXA-23, bla OXA-58 and bla OXA-24/40 were identified in 49 (41.5%), eight (6.8%) and one (0.8%) A. baumannii isolates, respectively. ISAba1 was found upstream of the gene (a bla OXA-51-like) in 10 isolates. Conclusions The emergence of OXA-23-producing ST1 and ST2, the predominant MDR lineages, shows a pivotal shift in carbapenem-resistant A. baumannii (CRAB) epidemiology in Spain. Coupled with increased colistin resistance, these changes underscore notable alterations in regional antimicrobial resistance dynamics.

and permissions as for the article apply.Supplements are not edited by Eurosurveillance, and the journal is not responsible for the maintenance of any links or email addresses provided therein.Table S2.MIC distributions and resistance rates to the antibiotics tested relative to the set of A. non-baumannii isolates, Spain, 2020 (n= 81).
a Actual MIC value is equal or inferior to that stated.
b Actual MIC value is equal or higher to that stated.c We considered sulbactam a beta-lactam in this context, due to its intrinsic antibacterial activity against Acinetobacter.

Class of antibiotic
Per cent of isolates at MIC (mg/L) % R ≤0.12 0.25 0.   a Actual MIC value is equal or inferior to that stated.
b Actual MIC value is equal or higher to that stated.
c We considered sulbactam a beta-lactam in this context, due to its intrinsic antibacterial activity against Acinetobacter.

Materials and Methods
Molecular typing, phylogenetic analysis and antimicrobial resistance analysis.
The assemblies were submitted to the PubMLST ribosomal Multilocus Sequence Typing (rMLST) database (https://pubmlst.org/)for species of Acinetobacter spp.and sequence type (ST) determination, following the Pasteur scheme [1].Additionally, CARD v3.2.5 and Resfinder v4.1 databases were used to assess the presence or absence of antimicrobial resistance (AMR) genetic determinants.The polished reads of A. baumannii were mapped against the reference genome A.
To assess genomic changes implicated in antimicrobial resistance in A. non-baumannii isolates, a comparison was performed against genomes of susceptible strains that were obtained from one of the following three methods: i) a susceptible strain from our collection, ii) ATCC strains with public genomes, or iii) a reference genome for that species in the NCBI taxonomy.Genes homologous to those cited above for A. baumannii were evaluated in the rest of the Acinetobacter spp.
Allele calling for A. baumannii was carried out using chewBBACCA [2], employing the cgMLST schema provided within the same software.After obtaining a cgMLST for the 118 A. baumannii isolates, a distance matrix was constructed based on the cgMLST allele calls to clarify the genomic relatedness among the isolates.A. baumannii isolates with a cgMLST (Allele) distance of ≤3 were categorized as closely related genomes [3].Hence, the threshold of ≤3 cgMLST (Allele) was established to assess potential clonal dissemination among isolates.The phylogenetic dendrogram was created using the Neighbor-joining model with Ape package v.5.7-1 [4] available in the CRAN repository.Lastly, the tree was visualized with Itol software [5].
Allele calling for A. non-baumannii species was not possible because the cgMLST scheme has not yet been developed.Therefore, analyses of the phylogenetic trees of these species were performed.
Genomes of A. non-baumannii were annotated using Bakta and then the core genome was identified using the pangenome clustering tool Panaroo [6].Finally, the resulting phylogenetic tree was constructed with with RAxML using GTR+G model (discrete GAMMA model of rate heterogeneity with 4 categories) [7] and visualized through Itols software.

Table S4 .
Species identification, isolation site, MIC values and putative resistance mechanisms for the set of A. non-baumannii isolates, Spain, 2020 (n=81). a,

Table S5 .
MIC distributions and resistance rates to the antibiotics tested, according to EUCAST v12.0 breakpoints and relative to the set of A.